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KMID : 1101120230200010028
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Journal of Sleep Medicine
2023 Volume.20 No. 1 p.28 ~ p.34
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Validation of the Samsung Smartwatch for Sleep?Wake Determination and Sleep Stage Estimation
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Kim Dong-Yeop
Joo Eun-Yeon
Choi Su-Jung
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Abstract
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Objectives: Galaxy Watch 3 (GW3) is a commercially available smartwatch equipped with a sleep-tracking function capable of collecting longitudinal sleep data in a real-world environment. We aimed to investigate the validity of GW3 for estimating sleep stages compared with reference data from polysomnography (PSG).
Methods: Thirty-two healthy adults (mean age 37.8, male 87.5%) were recruited to wear a GW3 concurrently with in-laboratory overnight PSG recording. Sleep parameters, including total sleep time (TST) and the duration of each sleep stage (light, deep, and rapid eye movement [REM] sleep), were calculated for both GW3 and PSG. Sleep parameters were compared using intraclass correlation coefficients (ICCs) and Bland?Altman plots. The epoch-by-epoch classification performance was evaluated to determine the sensitivity, specificity, accuracy, kappa values, and confusion matrices.
Results: Bland?Altman plots showed moderate agreement between GW3 and PSG for TST (ICC=0.640), light sleep (ICC=0.518), and deep sleep (ICC=0.639), whereas REM sleep duration was not reliably estimated using the GW3. The GW3 overestimated TST by a mean of 9.5 min. The sensitivity of epoch-by-epoch sleep detection was 0.954; however, the specificity was 0.524. The sensitivity of each sleep stage estimation was 0.695 for light sleep, 0.612 for deep sleep, and 0.598 for REM sleep. The overall accuracy of GW3 in distinguishing the four-stage sleep epochs was 0.651.
Conclusions: GW3 demonstrated high performance in sleep detection but moderate performance in wake determination and sleep stage estimation compared with PSG results, which were comparable to previously reported results for other consumer wearable devices.
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KEYWORD
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Wearable electronic devices, Actigraphy, Polysomnography, Sleep stages, Validation
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